This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers.
One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups.
Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10−3 ± 0.24 × 10−3 mm2/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10−3 ± 0.30 × 10−3 mm2/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10−3 ± 0.33 × 10−3 mm2/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10−3 ± 0.34 × 10−3 mm2/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10−3 mm2/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation.
T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.
From the *Department of Radiology, Mayo Clinic, Rochester;
†Case Western University School of Medicine, Departments of
∥Epidemiology and Biostatistics, Case Western Reserve University; and Departments of
#Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH.
Received for publication October 7, 2018; and accepted for publication, after revision, February 28, 2019.
Conflicts of interest and sources of funding: Authors Ananya Panda, Wei Ching Lo, Yun, Jiang, Mark Griswold, and Vikas Gulani received research support from Siemens Healthineers as part of a research grant to the University. The magnetic resonance fingerprinting technology has also been licensed by Siemens. Royalty payments have not yet started but are expected to start over the next 2 to 3 months. Other authors, namely, Gregory O'Connor, Seunghee Margevicius, Mark Schluchter, and Lee Ponsky, do not have industry grant support to report. Other funding sources included NIH grants 1R01CA208236, 1R01EB016728, 1R01DK098503, and 1R01EB017219. Funding for this research was received from the NIH (P41 EB 015898).
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Correspondence to: Vikas Gulani, MD, PhD, Department of Radiology, Case Western Reserve University, Bolwell Bldg, B120, 11100 Euclid Ave, Cleveland, OH 44106. E-mail: firstname.lastname@example.org.
Online date: April 15, 2019